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<div class="title">statistical_multiscale_interest_region_extraction.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2011, Alexandru-Eugen Ichim</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *  Copyright (c) 2012-, Open Perception, Inc.</span></div>
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<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">#ifndef PCL_FEATURES_IMPL_STATISTICAL_MULTISCALE_INTEREST_REGION_EXTRACTION_H_</span></div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#define PCL_FEATURES_IMPL_STATISTICAL_MULTISCALE_INTEREST_REGION_EXTRACTION_H_</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160; </div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;pcl/features/statistical_multiscale_interest_region_extraction.h&gt;</span></div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;pcl/kdtree/kdtree_flann.h&gt;</span></div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="common_2include_2pcl_2common_2distances_8h.html">pcl/common/distances.h</a>&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;pcl/features/boost.h&gt;</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">#include &lt;boost/graph/adjacency_list.hpp&gt;</span></div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="preprocessor">#include &lt;boost/graph/johnson_all_pairs_shortest.hpp&gt;</span></div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160; </div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160; </div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00053"></a><span class="lineno"><a class="line" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#a2acae9d282f9a39891bce7347c828ff3">   53</a></span>&#160;<a class="code" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#a2acae9d282f9a39891bce7347c828ff3">pcl::StatisticalMultiscaleInterestRegionExtraction&lt;PointT&gt;::generateCloudGraph</a> ()</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;{</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  <span class="comment">// generate a K-NNG (K-nearest neighbors graph)</span></div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;  <a class="code" href="classpcl_1_1_kd_tree_f_l_a_n_n.html">pcl::KdTreeFLANN&lt;PointT&gt;</a> kdtree;</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  kdtree.<a class="code" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#aba28a792bf0c2026aa0a6a99ed3e32ec">setInputCloud</a> (input_);</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160; </div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  <span class="keyword">using namespace </span>boost;</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  <span class="keyword">typedef</span> property&lt;edge_weight_t, float&gt; Weight;</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;  <span class="keyword">typedef</span> adjacency_list&lt;vecS, vecS, undirectedS, no_property, Weight&gt; Graph;</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  Graph cloud_graph;</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160; </div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> point_i = 0; point_i &lt; input_-&gt;points.size (); ++point_i)</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  {</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    std::vector&lt;int&gt; k_indices (16);</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    std::vector&lt;float&gt; k_distances (16);</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    kdtree.<a class="code" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a9bdbc03758c8d7b3033139e2fb1e6150">nearestKSearch</a> (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (point_i), 16, k_indices, k_distances);</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160; </div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k_i = 0; k_i &lt; static_cast&lt;int&gt; (k_indices.size ()); ++k_i)</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;      add_edge (point_i, k_indices[k_i], Weight (std::sqrt (k_distances[k_i])), cloud_graph);</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;  }</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> E = num_edges (cloud_graph),</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;      V = num_vertices (cloud_graph);</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;  PCL_INFO (<span class="stringliteral">&quot;The graph has %lu vertices and %lu edges.\n&quot;</span>, V, E);</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;  geodesic_distances_.clear ();</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; V; ++i)</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  {</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    std::vector&lt;float&gt; aux (V);</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    geodesic_distances_.push_back (aux);</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;  }</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;  johnson_all_pairs_shortest_paths (cloud_graph, geodesic_distances_);</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160; </div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  PCL_INFO (<span class="stringliteral">&quot;Done generating the graph\n&quot;</span>);</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;}</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160; </div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160; </div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#ac69e4773238c2f43c1647f9f2d357e52">   91</a></span>&#160;<a class="code" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#ac69e4773238c2f43c1647f9f2d357e52">pcl::StatisticalMultiscaleInterestRegionExtraction&lt;PointT&gt;::initCompute</a> ()</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;{</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_p_c_l_base.html">PCLBase&lt;PointT&gt;::initCompute</a> ())</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;  {</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::StatisticalMultiscaleInterestRegionExtraction::initCompute] PCLBase::initCompute () failed - no input cloud was given.\n&quot;</span>);</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  }</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  <span class="keywordflow">if</span> (scale_values_.empty ())</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  {</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::StatisticalMultiscaleInterestRegionExtraction::initCompute] No scale values were given\n&quot;</span>);</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;  }</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160; </div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;  <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;}</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160; </div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;<a class="code" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html">pcl::StatisticalMultiscaleInterestRegionExtraction&lt;PointT&gt;::geodesicFixedRadiusSearch</a> (<span class="keywordtype">size_t</span> &amp;query_index,</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;                                                                                       <span class="keywordtype">float</span> &amp;radius,</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;                                                                                       std::vector&lt;int&gt; &amp;result_indices)</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;{</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; geodesic_distances_[query_index].size (); ++i)</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="keywordflow">if</span> (i != query_index &amp;&amp; geodesic_distances_[query_index][i] &lt; radius)</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;      result_indices.push_back (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (i));</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;}</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160; </div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160; </div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#a1249e0fefdfb684e28e3ffb3c3427078">  122</a></span>&#160;<a class="code" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#a1249e0fefdfb684e28e3ffb3c3427078">pcl::StatisticalMultiscaleInterestRegionExtraction&lt;PointT&gt;::computeRegionsOfInterest</a> (std::list&lt;IndicesPtr&gt; &amp;rois)</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;{</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;  <span class="keywordflow">if</span> (!initCompute ())</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;  {</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;StatisticalMultiscaleInterestRegionExtraction: not completely initialized\n&quot;</span>);</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;  }</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160; </div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;  generateCloudGraph ();</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160; </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;  computeF ();</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160; </div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;  extractExtrema (rois);</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;}</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160; </div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160; </div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;<a class="code" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html">pcl::StatisticalMultiscaleInterestRegionExtraction&lt;PointT&gt;::computeF</a> ()</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;{</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;  PCL_INFO (<span class="stringliteral">&quot;Calculating statistical information\n&quot;</span>);</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160; </div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;  <span class="comment">// declare and initialize data structure</span></div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;  F_scales_.resize (scale_values_.size ());</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;  std::vector&lt;float&gt; point_density (input_-&gt;points.size ()),</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;          F (input_-&gt;points.size ());</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;  std::vector&lt;std::vector&lt;float&gt; &gt; phi (input_-&gt;points.size ());</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;  std::vector&lt;float&gt; phi_row (input_-&gt;points.size ());</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160; </div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> scale_i = 0; scale_i &lt; scale_values_.size (); ++scale_i)</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;  {</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    <span class="keywordtype">float</span> scale_squared = scale_values_[scale_i] * scale_values_[scale_i];</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160; </div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="comment">// calculate point density for each point x_i</span></div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> point_i = 0; point_i &lt; input_-&gt;points.size (); ++point_i)</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    {</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;      <span class="keywordtype">float</span> point_density_i = 0.0;</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> point_j = 0; point_j &lt; input_-&gt;points.size (); ++point_j)</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;      {</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;        <span class="keywordtype">float</span> d_g = geodesic_distances_[point_i][point_j];</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        <span class="keywordtype">float</span> phi_i_j = 1.0f / std::sqrt (2.0f * <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (M_PI) * scale_squared) * expf ( (-1) * d_g*d_g / (2.0f * scale_squared));</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160; </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        point_density_i += phi_i_j;</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;        phi_row[point_j] = phi_i_j;</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;      }</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;      point_density[point_i] = point_density_i;</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;      phi[point_i] = phi_row;</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    }</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160; </div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    <span class="comment">// compute weights for each pair (x_i, x_j), evaluate the operator A_hat</span></div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> point_i = 0; point_i &lt; input_-&gt;points.size (); ++point_i)</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    {</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;      <span class="keywordtype">float</span> A_hat_normalization = 0.0;</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;      <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> A_hat; A_hat.x = A_hat.y = A_hat.z = 0.0;</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> point_j = 0; point_j &lt; input_-&gt;points.size (); ++point_j)</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;      {</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;        <span class="keywordtype">float</span> phi_hat_i_j = phi[point_i][point_j] / (point_density[point_i] * point_density[point_j]);</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;        A_hat_normalization += phi_hat_i_j;</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160; </div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> aux = input_-&gt;points[point_j];</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;        aux.x *= phi_hat_i_j; aux.y *= phi_hat_i_j; aux.z *= phi_hat_i_j;</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160; </div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;        A_hat.x += aux.x; A_hat.y += aux.y; A_hat.z += aux.z;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      }</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;      A_hat.x /= A_hat_normalization; A_hat.y /= A_hat_normalization; A_hat.z /= A_hat_normalization;</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160; </div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;      <span class="comment">// compute the invariant F</span></div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;      <span class="keywordtype">float</span> aux = 2.0f / scale_values_[scale_i] * euclideanDistance&lt;PointT, PointT&gt; (A_hat, input_-&gt;points[point_i]);</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;      F[point_i] = aux * expf (-aux);</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    }</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160; </div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    F_scales_[scale_i] = F;</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;  }</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;}</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160; </div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160; </div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;<a class="code" href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html">pcl::StatisticalMultiscaleInterestRegionExtraction&lt;PointT&gt;::extractExtrema</a> (std::list&lt;IndicesPtr&gt; &amp;rois)</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;{</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;  std::vector&lt;std::vector&lt;bool&gt; &gt; is_min (scale_values_.size ()),</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;      is_max (scale_values_.size ());</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160; </div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;  <span class="comment">// for each point, check if it is a local extrema on each scale</span></div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> scale_i = 0; scale_i &lt; scale_values_.size (); ++scale_i)</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;  {</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    std::vector&lt;bool&gt; is_min_scale (input_-&gt;points.size ()),</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        is_max_scale (input_-&gt;points.size ());</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> point_i = 0; point_i &lt; input_-&gt;points.size (); ++point_i)</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;    {</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;      std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;      geodesicFixedRadiusSearch (point_i, scale_values_[scale_i], nn_indices);</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;      <span class="keywordtype">bool</span> is_max_point = <span class="keyword">true</span>, is_min_point = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;      <span class="keywordflow">for</span> (std::vector&lt;int&gt;::iterator nn_it = nn_indices.begin (); nn_it != nn_indices.end (); ++nn_it)</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        <span class="keywordflow">if</span> (F_scales_[scale_i][point_i] &lt; F_scales_[scale_i][*nn_it])</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;          is_max_point = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;        <span class="keywordflow">else</span></div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;          is_min_point = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160; </div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;      is_min_scale[point_i] = is_min_point;</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;      is_max_scale[point_i] = is_max_point;</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    }</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160; </div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    is_min[scale_i] = is_min_scale;</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    is_max[scale_i] = is_max_scale;</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;  }</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160; </div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;  <span class="comment">// look for points that are min/max over three consecutive scales</span></div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> scale_i = 1; scale_i &lt; scale_values_.size () - 1; ++scale_i)</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;  {</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> point_i = 0; point_i &lt; input_-&gt;points.size (); ++point_i)</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;      <span class="keywordflow">if</span> ((is_min[scale_i - 1][point_i] &amp;&amp; is_min[scale_i][point_i] &amp;&amp; is_min[scale_i + 1][point_i]) ||</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;          (is_max[scale_i - 1][point_i] &amp;&amp; is_max[scale_i][point_i] &amp;&amp; is_max[scale_i + 1][point_i]))</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;        {</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        <span class="comment">// add the point to the result vector</span></div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;        IndicesPtr region (<span class="keyword">new</span> std::vector&lt;int&gt;);</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;        region-&gt;push_back (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (point_i));</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160; </div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        <span class="comment">// and also add its scale-sized geodesic neighborhood</span></div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;        std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        geodesicFixedRadiusSearch (point_i, scale_values_[scale_i], nn_indices);</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;        region-&gt;insert (region-&gt;end (), nn_indices.begin (), nn_indices.end ());</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        rois.push_back (region);</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;      }</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;  }</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;}</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160; </div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160; </div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;<span class="preprocessor">#define PCL_INSTANTIATE_StatisticalMultiscaleInterestRegionExtraction(T) template class PCL_EXPORTS pcl::StatisticalMultiscaleInterestRegionExtraction&lt;T&gt;;</span></div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160; </div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* PCL_FEATURES_IMPL_STATISTICAL_MULTISCALE_INTEREST_REGION_EXTRACTION_H_ */</span><span class="preprocessor"></span></div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160; </div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_f_l_a_n_n_html"><div class="ttname"><a href="classpcl_1_1_kd_tree_f_l_a_n_n.html">pcl::KdTreeFLANN</a></div><div class="ttdoc">KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. The class is making use...</div><div class="ttdef"><b>Definition:</b> kdtree_flann.h:70</div></div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_f_l_a_n_n_html_a9bdbc03758c8d7b3033139e2fb1e6150"><div class="ttname"><a href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a9bdbc03758c8d7b3033139e2fb1e6150">pcl::KdTreeFLANN::nearestKSearch</a></div><div class="ttdeci">int nearestKSearch(const PointT &amp;point, int k, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances) const</div><div class="ttdoc">Search for k-nearest neighbors for the given query point.</div><div class="ttdef"><b>Definition:</b> kdtree_flann.hpp:132</div></div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_f_l_a_n_n_html_aba28a792bf0c2026aa0a6a99ed3e32ec"><div class="ttname"><a href="classpcl_1_1_kd_tree_f_l_a_n_n.html#aba28a792bf0c2026aa0a6a99ed3e32ec">pcl::KdTreeFLANN::setInputCloud</a></div><div class="ttdeci">void setInputCloud(const PointCloudConstPtr &amp;cloud, const IndicesConstPtr &amp;indices=IndicesConstPtr())</div><div class="ttdoc">Provide a pointer to the input dataset.</div><div class="ttdef"><b>Definition:</b> kdtree_flann.hpp:92</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase</a></div><div class="ttdoc">PCL base class. Implements methods that are used by most PCL algorithms.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:69</div></div>
<div class="ttc" id="aclasspcl_1_1_statistical_multiscale_interest_region_extraction_html"><div class="ttname"><a href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html">pcl::StatisticalMultiscaleInterestRegionExtraction</a></div><div class="ttdoc">Class for extracting interest regions from unstructured point clouds, based on a multi scale statisti...</div><div class="ttdef"><b>Definition:</b> statistical_multiscale_interest_region_extraction.h:66</div></div>
<div class="ttc" id="aclasspcl_1_1_statistical_multiscale_interest_region_extraction_html_a1249e0fefdfb684e28e3ffb3c3427078"><div class="ttname"><a href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#a1249e0fefdfb684e28e3ffb3c3427078">pcl::StatisticalMultiscaleInterestRegionExtraction::computeRegionsOfInterest</a></div><div class="ttdeci">void computeRegionsOfInterest(std::list&lt; IndicesPtr &gt; &amp;rois)</div><div class="ttdoc">The method to be called in order to run the algorithm and produce the resulting set of regions of int...</div><div class="ttdef"><b>Definition:</b> statistical_multiscale_interest_region_extraction.hpp:122</div></div>
<div class="ttc" id="aclasspcl_1_1_statistical_multiscale_interest_region_extraction_html_a2acae9d282f9a39891bce7347c828ff3"><div class="ttname"><a href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#a2acae9d282f9a39891bce7347c828ff3">pcl::StatisticalMultiscaleInterestRegionExtraction::generateCloudGraph</a></div><div class="ttdeci">void generateCloudGraph()</div><div class="ttdoc">Method that generates the underlying nearest neighbor graph based on the input point cloud</div><div class="ttdef"><b>Definition:</b> statistical_multiscale_interest_region_extraction.hpp:53</div></div>
<div class="ttc" id="aclasspcl_1_1_statistical_multiscale_interest_region_extraction_html_ac69e4773238c2f43c1647f9f2d357e52"><div class="ttname"><a href="classpcl_1_1_statistical_multiscale_interest_region_extraction.html#ac69e4773238c2f43c1647f9f2d357e52">pcl::StatisticalMultiscaleInterestRegionExtraction::initCompute</a></div><div class="ttdeci">bool initCompute()</div><div class="ttdoc">Checks if all the necessary input was given and the computations can successfully start</div><div class="ttdef"><b>Definition:</b> statistical_multiscale_interest_region_extraction.hpp:91</div></div>
<div class="ttc" id="acommon_2include_2pcl_2common_2distances_8h_html"><div class="ttname"><a href="common_2include_2pcl_2common_2distances_8h.html">distances.h</a></div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_r_g_b_a_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates, and the RGBA color.</div><div class="ttdef"><b>Definition:</b> point_types.hpp:540</div></div>
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